@inbook{08ee7288ba6f4d9f8c13ccca6d5ca471,
title = "Dealing with Missing Data in an IPD Meta‐Analysis",
abstract = "This chapter provides an overview of different methods for dealing with missing data in an individual participant data (IPD) meta-analysis. It highlights the specific challenges of dealing with missing data in an IPD meta-analysis context, including how to preserve the clustering of participants within primary studies, whilst allowing for potential between-study heterogeneity. The describes the various types of missing data that can occur in an IPD meta-analysis project, and the strategies, statistical approaches and software to deal with each. It focuses on dealing with missing data in the context of IPD meta-analyses of observational studies, for example for examining prognostic factors or developing prediction models. A number of prognostic factors ({\textquoteleft}predictors{\textquoteright}) are known to be associated with the incidence of preeclampsia; for example, a woman has a higher risk if she had pre-eclampsia in a previous pregnancy, or if there is a family history of pre-eclampsia, diabetes, or renal disease.",
author = "Debray, {Thomas P. A.} and Snell, {Kym I. E.} and Matteo Quartagno and Shahab Jolani and K.G.M. Moons and Riley, {Richard D.}",
note = "Publisher Copyright: {\textcopyright} 2021 John Wiley & Sons Ltd. All rights reserved.",
year = "2021",
language = "English",
isbn = "978-1-119-33372-2",
series = "Statistics in Practice Series",
pages = "499--524",
editor = "Riley, {Richard D. } and Tierney, {Jayne F. } and Stewart, {Lesley A. }",
booktitle = "Individual Participant Data Meta‐Analysis",
publisher = "Wiley",
}